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Concise and interpretable multi-label rule sets
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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38
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Knowledge and Information Systems, Volume 65, issue 12, pp. 5657-5694
Abstract
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of simple “if-then” rules, and thus, it offers better interpretability compared to black-box models. Notably, our method is able to find a small set of relevant patterns that lead to accurate multi-label classification, while existing rule-based classifiers are myopic and wasteful in searching rules, requiring a large number of rules to achieve high accuracy. In particular, we formulate the problem of choosing multi-label rules to maximize a target function, which considers not only discrimination ability with respect to labels, but also diversity. Accounting for diversity helps to avoid redundancy, and thus, to control the number of rules in the solution set. To tackle the said maximization problem, we propose a 2-approximation algorithm, which circumvents the exponential-size search space of rules using a novel technique to sample highly discriminative and diverse rules. In addition to our theoretical analysis, we provide a thorough experimental evaluation and a case study, which indicate that our approach offers a trade-off between predictive performance and interpretability that is unmatched in previous work.
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Funding Information: This research is supported by the Academy of Finland project MLDB (325117), the ERC Advanced Grant REBOUND (834862), the EC H2020 RIA project SoBigData++ (871042), and the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. | openaire: EC/H2020/871042/EU//SoBigData-PlusPlus
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Ciaperoni, M, Xiao, H & Gionis, A 2023, 'Concise and interpretable multi-label rule sets', Knowledge and Information Systems, vol. 65, no. 12, pp. 5657-5694. https://doi.org/10.1007/s10115-023-01930-6